Adjusting execution of tasks in a dispersed storage network

ABSTRACT

A method includes a set of execution units of a dispersed storage network (DSN) receiving sets of sub-task requests from a computing device and storing the sets of sub-task requests, where each execution unit stores a request of each of the sets of sub-task requests to produce a corresponding plurality of sub-task requests. The method continues with each execution unit generating sub-task estimation data and adjusting timing, sequencing, or processing of the corresponding plurality of sub-task requests based on the estimation data to produce a plurality of partial results, where, due to one or more difference factors from a list of difference factors, the execution units process pluralities of sub-task requests at difference paces, where the list of difference factors includes differences in amounts of data to be processed per sub-task request, processing capabilities, memory storage capabilities, and networking capabilities.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 61/886,452,entitled “ACCESSING A VAULT OF A DISPERSED STORAGE NETWORK”, filed Oct.3, 2013, which is hereby incorporated herein by reference in itsentirety and made part of the present U.S. Utility Patent Applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound

DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) sub-vault in accordance with the presentinvention;

FIG. 41A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 41B is a flowchart illustrating an example of authenticating arequesting entity in accordance with the present invention;

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 42B is a diagram illustrating an example of generation mapping tostorage units in accordance with the present invention;

FIG. 42C is a flowchart illustrating an example of commissioning storageunits in accordance with the present invention;

FIGS. 43A, 43B and 43C are diagrams illustrating further examples ofgeneration mapping to storage units in accordance with the presentinvention;

FIG. 43D is a flowchart illustrating another example of commissioningstorage units in accordance with the present invention;

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44B is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit in accordance with thepresent invention;

FIG. 44C is a flowchart illustrating an example of accessing encodeddata slices in accordance with the present invention;

FIG. 44D is a flowchart illustrating an example of listing encoded dataslices in accordance with the present invention;

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 45B is a flowchart illustrating an example of canceling a requestin accordance with the present invention;

FIG. 45C is another flowchart illustrating an example of canceling arequest in accordance with the present invention;

FIGS. 46A-D are schematic block diagrams of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 46E is a flowchart illustrating an example of adjusting executionof tasks in accordance with the present invention;

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 47B is a flowchart illustrating an example of processing a requestin accordance with the present invention;

FIGS. 48A-B are schematic block diagrams of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 48C is a flowchart illustrating an example of migrating encodeddata slices in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as 10 ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 &d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 &d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 &d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4);the content of the second encoded data slice (DS2_d18&19) of the secondset of encoded data slices is substantially similar to content of thesecond word (e.g., d18 &d19); and the content of the third encoded dataslice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 &d34). Thecontent of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3). The errordecoding module 206 decodes the encoded data segments in accordance witherror correction decoding parameters received as control information 190from the control module 186 to produce secure data segments. The errorcorrection decoding parameters include identifying an error correctionencoding scheme (e.g., forward error correction algorithm, aReed-Solomon based algorithm, an information dispersal algorithm, etc.),a pillar width, a decode threshold, a read threshold, a write threshold,etc. For example, the error correction decoding parameters identify aspecific error correction encoding scheme, specify a pillar width offive, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5-compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words).

Task 1_2 (e.g., identify unique words) has similar task executioninformation as task 1_1 to produce task 1_2 intermediate results (R1-2,which is the list of unique words). Task 1_3 (e.g., translate) includestask execution information as being non-ordered (i.e., is independent),having DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate datapartitions 2_1 through 2_4 and having DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 translate data partitions 2_5 through 2_z to producetask 1_3 intermediate results (R1-3, which is the translated data). Inthis example, the data partitions are grouped, where different sets ofDT execution modules perform a distributed sub-task (or task) on eachdata partition group, which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1st through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the user device 14, the distributedstorage and task (DST) processing unit 16, the network 24, and thedistributed storage and task network (DSTN) module 22 of FIG. 1. TheDSTN module 22 includes a set of DST execution units 1-n. The set of DSTexecution units 1-n includes the DST execution units 36 of FIG. 1.

In an embodiment, the dispersed storage and task (DST) processing unit16 includes at least one module, such as DST client module 34, whenoperable within a computing device, that causes the computing device toperform the following method steps: receiving a data access request forat least one data segment stored in the DSN; identifying a vaultassociated with the data access request and one of a plurality ofsub-vaults associated with the vault; retrieving access informationcorresponding to the one of the plurality of sub-vaults to determinewhether the data access request is allowed; and when the data accessrequest is allowed, retrieving a sub-vault directory associated with theone of the plurality of sub-vaults to identify a DSN addresscorresponding to the data access request, and fulfilling the data accessrequest based on with the DSN address.

In an embodiment, the data access request includes a requesting entityidentifier (ID) and the vault is identified based on the requestingentity ID. The one of the plurality of sub-vaults can be identifiedbased on an identification of the vault and based on a sub-vault IDincluded in the data access request. Retrieving the access informationcorresponding to the one of the plurality of sub-vaults can includeretrieving a vault directory corresponding to the vault associated withthe data access request. The vault directory can indicate an address foreach of the plurality of sub-vaults associated with the vault.Determining whether the data access request is allowed can includedetermining a request type associated with the data access request andallowing the data access request when the access information indicatesthe request type is allowed. The data access request can indicate therequest type as one of: a read request, or a write request.

The method described above in conjunction with the DST processing unit16 can alternatively be performed by other modules of a dispersedstorage network, of a dispersed storage and tracking network or by otherdevices. In addition, at least one memory section that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), cause the one or more computing devices to perform any or all ofthe method steps described above.

In an example of operation shown, the DST processing unit 16 receives adata access request 400 from the user device 14. The data access request400 includes one or more of a requesting entity identifier (ID) field402, a data name field 404, a sub-vault ID field 406, a request typefield 408, and a data object when the data access request corresponds toa write request. The requesting entity ID field 402 includes arequesting entity ID entry corresponding to at least one of the userdevice 14 and a user of the user device 14. As a specific example, therequesting entity ID is 457 corresponding to the user of the user device14. The data name field 404 includes a data name entry corresponding tothe data object associated with the data access request, where the dataname is in accordance with a data naming convention associated with theuser device 14. As a specific example, the data name entry is “foo”corresponding to the filename of the data object. The sub-vault ID fieldincludes a sub-vault ID entry 406 corresponding to a sub-vaultassociated with the user, where a vault associated with the userincludes one or more sub-vaults that include the sub-vault. A vaultincludes a virtual storage entity associated with one or more users.Alternatively, or in addition to, the data access request includes asub-vault name (e.g., “John's sub-vault”) associated with the sub-vaultID 406. The request type field 408 includes a request type entrycorresponding to a type of data access request (e.g., write, read, list,delete, etc.) associated with the data object. As a specific example,the request type entry includes a read access type.

Having received the data access request 400, the DST processing unit 16identifies a vault associated with the data access request 400. Theidentifying includes obtaining a vault list 416 and extracting a vaultID 420 corresponding to the requesting entity ID entry 418. As aspecific example, the DST processing unit 16 issues requests (e.g.,requests 1-n) to the set of DST execution units to retrieve at least oneset of vault list slices, receives at least a decode threshold number ofvault list slices in responses from the set of DST execution units,decodes the decode threshold number of vault list slices to produce thevault list, accesses the vault list 416 using the requesting entity IDentry 418 to extract the vault ID 420.

Having identified the vault ID 420, the DST processing unit 16determines whether the data access request 400 is allowed based onsub-vault access information 428 of a vault directory 422. The vaultdirectory 422 includes a vault ID field 424, a sub-vault ID field 426, asub-vault access information field 428, and a sub-vault directoryaddress field 430. The vault ID field 424 includes a vault ID entry. Thesub-vault ID field 426 includes a sub-vault ID entry. The sub-vaultaccess information field 428 includes one or more sub-vault accessinformation entries. Each sub-vault access information entry indicatesone or more access rights (e.g., allowed request types for all orindividual data names associated with one or more sub-vaults of a vaultfor at least one user and/or user device. As a specific example ofdetermining whether the data access request 400 is allowed, the DSTprocessing unit 16 obtains a vault directory 422 for the vault ID 424(e.g., issues requests 410 to the set of DST execution units, receivesresponses 412, decodes vault directory slices to reproduce the vaultdirectory) extracts the sub-vault access information 428 from the vaultdirectory 422 based on the sub-vault ID 426, and compares the requestingentity ID 402 and request type 408 to the sub-vault access information428 to determine whether the data access request 400 is allowed. As aspecific example, the DST processing unit 16 indicates that the dataaccess request 400 is allowed when the sub-vault access information 428indicates that user 457 is allowed to read sub-vault 2.

When the DST processing unit 16 determines that the data access request428 is not allowed, the DST processing unit 16 issues a data accessresponse 414 to the user device 14 indicating that the data accessrequest 400 is not allowed. When the DST processing unit 16 determinesthat the data access request 400 is allowed, the DST processing unit 16accesses a sub-vault directory to identify a DSN address 434 for thedata access request 400. As a specific example, the DST processing unit16 extracts a sub-vault directory address 430 from the vault directory422, accesses the DSTN module 22 utilizing the sub-vault directoryaddress 430 to recover the sub-vault directory, and extracts the DSNaddress 434 from the sub-vault directory utilizing the data name 432.For instance, the DST processing unit 16 extracts sub-vault directoryaddress 91AB from the vault directory 422, accesses the DSTN module 22at DSN address 91AB to recover sub-vault A-2 directory, and extracts DSNaddress 88C3 corresponding to the data name “foo.” Having identified theDSN address 434 for the data access request 400, the DST processing unit16 accesses the DSTN module 22 utilizing the DSN address 434 to processthe data access request 400. The DST processing unit 16 issues a dataaccess response 414 to the user device 14, where the data accessresponse 414 is based on accessing the DSN address.

FIG. 40B is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) sub-vault. The method includes step 440 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) receives a data access request. The data access request includesone or more of requesting entity identifier (ID), a data name, asub-vault ID, a sub-vault name, and a request type. The method continuesat step 442 where the processing module identifies a vault associatedwith the data access request. As a specific example, the processingmodule obtains a vault list and performs a vault ID look up using therequesting entity ID to identify the vault ID.

The method continues at step 444 where the processing module obtains avault directory that includes sub-vault access information. As aspecific example, the processing module obtains (e.g., retrieves slices,decode slices) a vault directory that corresponds to the vault ID. Themethod continues at step 446 where the processing module determineswhether the data access request is allowed based on the sub-vault accessinformation. As a specific example, the processing module determineswhether the request type of the data access request is allowed for therequesting entity based on the sub-vault access information.

When the data access request is allowed, the method continues at step448 where the processing module accesses a sub-vault directory toidentify a DSN address corresponding to the data access request. As aspecific example, the processing module obtains a sub-vault directoryaddress from the vault directory, retrieves the sub-vault directoryusing the sub-vault directory address (e.g., retrieved slices, decodeslices), and extracts the DSN address corresponding to the data accessrequest from the sub-vault directory.

Having identified the DSN address, the method continues at step 450where the processing module accesses data utilizing the DSN address inaccordance with the data access request. As a specific example, theprocessing module issues one or more sets of read slice requests usingthe DSN address to retrieve slices when the data access request includesa read request. As another specific example, the processing moduleissues one or more sets of write slice requests using the DSN address,where the write slice requests includes encoded data slices encoded fromdata of the data access request when the data access request includes awrite request.

FIG. 41A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes at least one identity unit480, the user device 14, the distributed storage and task (DST)processing unit 16, the network 24, and the distributed storage and tasknetwork (DSTN) module 22 of FIG. 1. The DSTN module 22 includes a set ofDST execution units 1-n. The set of DST execution units 1-n includes theDST execution units 36 of FIG. 1.

In an embodiment, the dispersed storage and task (DST) processing unit16 includes at least one module, such as DST client module 34, whenoperable within a computing device, that causes the computing device toperform the following method steps: receiving a data access request forat least one data segment stored in the DSN; selecting one of aplurality of identity units based on the data access request;determining, via the selected one of the plurality of identity units,whether to allow the data access request; and processing the data accessrequest, when the data access request is allowed via the selected one ofthe plurality of identity units.

In an embodiment, the data access request includes requesting entitycredentials and indicates a request type. The one of the plurality ofidentity units can be selected based on at least one of: the requestingentity credentials, or the request type. Determining whether to allowthe data access request can include reviewing the requesting entitycredentials via the selected one of the plurality of identity units.When the data access request is allowed, processing the data accessrequest can be further conditioned on whether the requesting entity isauthorized to perform the request type. When the data access request isallowed, the data access request can be denied when the requestingentity is not authorized to perform the request type. The method canfurther include denying the data access request, when the data accessrequest is not allowed via the selected one of the plurality of identityunits.

The method described above in conjunction with the DST processing unit16 can alternatively be performed by other modules of a dispersedstorage network, of a dispersed storage and tracking network or by otherdevices. In addition, at least one memory section that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), cause the one or more computing devices to perform any or all ofthe method steps described above.

In an example of operation shown, the DST processing unit 16 receives adata access request 460 from the user device 14. The data access request460 includes a requesting entity credentials field 462 and a datarequest field 464. The requesting entity credentials field 462 includesone or more requesting entity credential entries (e.g., a requestingentity identifier (ID), a requesting entity name, a password, and asigned certificate). The data request field 464 includes one or moredata request entries (e.g., a request type, a data name, data).

Having received the data access request 460, the DST processing unit 16selects an identity unit 480 based on one or more of the requestingentity credentials 462 and a request type 464 of the data access request460. As a specific example, the DST processing unit 16 selects a firstidentity unit associated with the requesting entity identifier. Asanother specific example, the DST processing unit selects a secondidentity unit associated with a write request type of the data request.Having identified the identity unit 480, the DST processing unit 16issues an identity request 470 to the selected identity unit 480, wherethe identity request 470 includes the requesting entity credentials 462.The identity unit 480 receives the identity request 470, where theidentity request 470 is associated with at least one of the data accessrequest 460 and a slice access request. The identity unit 480 determineswhether the requesting entity credentials 462 are valid (e.g., validwhen a password matches a requesting entity ID, valid when a signedcertificate includes a valid signature by a valid authorizing signingentity). The identity unit 480 issues an identity response 472 to theDST processing unit 16 indicating whether the requesting entity has beenauthenticated or not authenticated. The identity unit 480 indicates thatthe requesting entity has been authenticated when the requesting entitycredentials 462 are valid.

The DST processing unit 16 receives the identity response 472 from theselected identity unit 480. Having received the identity response 472,the DST processing unit 16 determines whether the identity response 472is favorable (e.g., authenticated). When the identity response 472 isfavorable, the DST processing unit 16 processes the data access request460. The processing of the data access request 460 may further includeauthorizing a request type of the data request 464 based on therequesting entity identifier and access control information thatindicates whether the requesting entity is authorized to perform therequest type. The processing of the data access request further includesissuing requests 482 (e.g., slice requests 1-n) to the set of DSTexecution units 1-n, processing responses 484 (e.g., slices 1-n) fromthe DST execution units, and issuing a data access response 474 to theuser device 14 based on the processing of the responses (e.g., a statuswhen the data request includes a write data access request, a dataobject when the data request includes a re-data access request).

FIG. 41B is a flowchart illustrating an example of authenticating arequesting entity. The method includes step 490 where a processingmodule (e.g., of a distributed storage and task (DST) client module)receives an access request (e.g., a data access request, a slice accessrequest) that includes requesting entity credentials. The methodcontinues at step 492 where the processing module selects an identityunit based on the access request. As a specific example, the processingmodule selects the identity unit based on a requesting entity ID and amapping of requesting entity identifiers to various identity units. Asanother specific example, the processing module selects the identityunit based on a request type of the access request.

The method continues at step 494 where the processing module issues anidentity request to the selected identity unit. The issuing includesgenerating the identity request to include the requesting entitycredentials and sending the identity request to the selected identityunit. The method continues at step 496 where the processing modulereceives an identity response from the selected identity unit. When theidentity response is favorable (e.g., the requesting entity isauthenticated), the method continues at step 498 where the processingmodule processes the access request (e.g., write, read, delete, list,etc.). The processing may further include authorizing the access requestbased on the requesting entity ID and access control information.

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16, the network 24, and the distributedstorage and task network (DSTN) module 22 of FIG. 1. The DSTN module 22includes a plurality of DST execution units 36 of FIG. 1.

In an embodiment, the dispersed storage and task (DST) processing unit16 includes at least one module, such as DST client module 34, whenoperable within a computing device, that causes the computing device toperform the following method steps: storing a set of encoded data slicesin an original plurality of storage units of the DSN associated as acurrent generation of a storage vault; determining whether utilizationof the original plurality of storage units is greater than a utilizationthreshold; when the utilization of the original plurality of storageunits is greater than the utilization threshold, updating the originalplurality of storage units to include at least one additional storageunit and a proper subset of the original plurality of storage unitsassociated as the current generation of a storage vault to generate anupdated plurality of storage units associated as a next generation ofthe storage vault; and storing the set of encoded data slices in theupdated plurality of storage units of the DSN associated as the nextgeneration of the storage vault.

In an embodiment, the original plurality of storage units includes Nstorage units wherein N is an integer greater than 2 and wherein N−1 ofthe original plurality of storage units are included in the updatedplurality of storage units. The value of N can be based on or correspondto a write threshold number of encoded data slices for a data segmentstored in the DSN in at least one of the set of encoded data slices. Theoriginal plurality of storage units can be associated as the currentgeneration of the storage vault, via a table. Determining whetherutilization of the original plurality of storage units is greater thanthe utilization threshold can include determining whether utilization ofany one of the original plurality of storage units is greater than theutilization threshold. The proper subset of the original plurality ofstorage units can be determined by including ones of the originalplurality of storage units having utilization that is less than theutilization threshold. The set of encoded data slices can be stored inthe updated plurality of storage units, such that no one encoded dataslice of the set of encoded data slices is stored in a storage unit ofthe updated plurality of storage units used to store the one encodeddata slice of the set of encoded data slices in the original pluralityof storage units.

The method described above in conjunction with the DST processing unit16 can alternatively be performed by other modules of a dispersedstorage network, of a dispersed storage and tracking network or by otherdevices. In addition, at least one memory section that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), cause the one or more computing devices to perform any or all ofthe method steps described above.

In an example of operation shown, the DST processing unit 16 encodes adata object using a dispersed storage error coding function to produce aplurality of sets of encoded data slices. The DST processing unit 16generates a plurality of sets of slice names 500 corresponding to theplurality of sets of encoded data slices. Each slice name 500 includesone or more of a slice index field 502, a vault identifier (ID) field504, a vault generation field 506, and an object ID field 508, and asegment number field 510. The slice index field 502 includes a sliceindex entry (e.g., a pillar number of dispersal parameters associatedwith the dispersed storage error coding function) corresponding to acorresponding encoded data slice of each set of encoded data slices. Asa specific example, the slice index entry includes at least one of 1-4when a pillar width dispersal parameter of the dispersed storage errorcoding function is 4 and each set of encoded data slices includes fourencoded data slices. The vault ID field 504 includes a vault ID entry,where the vault ID entry corresponds to at least one of the data objectand/or a requesting entity requesting storage of the data object.

The vault generation field 506 includes a vault generation entry, wherethe vault generation entry designates a generation of one or moregenerations associated with a common vault. As a specific example, theDST processing unit 16 generates encoded data slices associated with afirst generation of vault A until which time the first generation isconsidered full. Subsequent to the filling of the first generation, theDST processing unit 16 generates encoded data slices associated with asecond-generation of vault A. The object ID field 508 includes an objectID entry that is uniquely associated with the data object. As a specificexample, the DST processing unit 16 generates the object ID entry bygenerating a random number. As another specific example, the DSTprocessing unit 16 generates the object ID entry by performing adeterministic function on an identifier associated with the data object.The segment number field includes a segment number entry, where eachsegment number entry is associated with a set of the plurality of setsof encoded data slices. As a specific example, a segment number entry of1 corresponds to a first set of encoded data slices and a segment numberentry of 2 corresponds to a second set of encoded data slices, etc.

Having generated the plurality of sets of encoded data slices and theplurality of sets of corresponding slice names, the DST processing unit16 issues one or more sets of write slice requests 512 to DST executionunits 36. As a specific example, the DST processing unit 16 issues 4write slice requests 512 to the DSTN module 22, where a first writeslice request includes encoded data slices and slice names correspondingto slice index 1, a second write slice request includes encoded dataslices and slice names corresponding to slice index 2, etc.

When utilization of storage capacity of the DST execution units 36utilized to store encoded data slices associated with the first vaultgeneration is greater than a maximum utilization threshold level, theDST processing unit 16 commissions one or more new DST execution units36 and establishes another vault generation for storage of further setsof encoded data slices associated with the common vault ID of A. As aspecific example, the DST processing unit 16 commissions a width number(e.g., 4) of DST execution units 36, where each of the four new DSTexecution units 36 is associated with one of the slice indexes 1-4 forthe second vault generation. As another specific example, the DSTprocessing unit 16 commissions one new DST execution unit 36, wherethree of an original four DST execution units 36 and the one new DSTexecution unit 36 are associated with encoded data slices of sliceindexes 1-4 for the second-generation vault. The commissioning of newDST execution units 36 and assignment of another vault generation isdiscussed in greater detail with reference to FIGS. 42B-C.

FIG. 42B is a diagram illustrating an example of generation mapping tostorage units that includes four original storage units (SU) 1-4 andsubsequently added storage units 5-12. The four original storage units1-4 are mapped to encoded data slices of slice names including sliceindexes 1-4 for a first generation of a common vault when a widthdispersal parameter of a dispersed storage error encoding function is 4.As more sets of encoded data slices associated with the first generationare stored to the original storage units 1- 4, a utilization level iscompared to a utilization level threshold. When the utilization level ofstorage capacity for the first generation is greater than theutilization level threshold, an additional storage unit (e.g., SU5) iscommissioned and a first (e.g., SU1) of the original for storage unitsis omitted from a mapping of a second-generation of the vault to storageunits such that SU2 is mapped to slice index 1, SU3 is mapped to sliceindex 2, SU4 is mapped to slice index 3, and SU5 is mapped to sliceindex 4 of the second-generation. When the utilization level of storagecapacity for the second-generation is greater than another utilizationlevel threshold, an additional storage unit (e.g., SU6) is commissionedand a third-generation of the vault is mapped to storage units 3-6.

With such a mapping, unutilized storage capacity occurs for a width-1number of the original storage units and temporarily for a newestwidth-1 number of storage units. The process may continue indefinitelysuch that a minimum of one additional new storage unit is commissionedwhen a storage utilization level is greater than a storage utilizationthreshold level.

FIG. 42C is a flowchart illustrating an example of commissioning storageunits. The method includes step 520 where a processing module (e.g., ofa distributed storage and task (DST) client module) initializescommissioning of a pillar width number of storage units to form acurrent set of storage units. The commissioning includes at least one ofselecting available storage units, receiving a storage unit assignmentinformation, initiating a query, receiving system manager storage unitselection input, and activating the set of storage units (e.g.,programming, configuring, activating for utilization within a dispersedstorage network). The method continues at step 522 where the processingmodule associates the current set of storage units with a firstgeneration of a storage vault as a current generation of the storagevault. As a specific example, the processing module updates a storagetable to associate the current set of storage units with the firstgeneration of the storage vault.

The method continues at step 524 where the processing module stores setsof encoded data slices of the current generation of the vault in thecurrent set of storage units. As a specific example, the processingmodule encodes data objects using a dispersed storage error codingfunction to produce pluralities of sets of encoded data slices andgenerates sets of slice names corresponding to the sets of encoded dataslices, where each slice name includes a common vault identifier and afirst-generation identifier.

The method continues at step 526 where the processing module determineswhether utilization of the current set of storage units is greater thana maximum utilization threshold level. As a specific example, theprocessing module initiates a query to obtain a current utilizationlevel, compares the current utilization level to the maximum utilizationthreshold level, and indicates that the utilization of the current setof storage units is greater than the maximum utilization threshold levelwhen the current utilization level is greater than the maximumutilization threshold level. The method branches to step 528 where theprocessing module commissions an additional storage unit when theutilization of the current set of storage units is greater than themaximum utilization threshold level. The method loops back to the step524 where the processing module stores the sets of encoded data sliceswhen the processing module determines that the utilization of thecurrent set of storage units is not greater than the maximum utilizationthreshold level.

When the utilization of the current set of storage units is greater thanthe maximum utilization threshold level, the method continues at step528 where the processing module commissions an additional storage unitand associates the additional storage unit with at least some storageunits of the current set of storage units to update the current set ofstorage units. As a specific example, the processing module activates adormant storage unit and associates the activated storage unit with awidth minus one number of the current set of storage units to produce anupdated current set of storage units.

The method continues at step 530 where the processing module associatesthe current set of storage units with a next generation of the storagevault to update the current generation of a storage vault. As a specificexample, the processing module identifies the next generation as thecurrent generation plus one and updates the storage table to associatethe current set of storage units with a next generation. The methodbranches back to the step 524 where the processing module stores sets ofencoded data slices.

FIGS. 43A, 43B and 43C are diagrams illustrating further examples ofgeneration mapping to storage units that include previous storage units(SU) 1-4 and a new storage units 5-8.

In an embodiment, the dispersed storage and task (DST) processing unit16 includes at least one module, such as DST client module 34, whenoperable within a computing device, that causes the computing device toperform the following method steps: storing a set of encoded data slicesin an original plurality of storage units of the DSN associated as acurrent generation of a storage vault; determining whether to increase awidth dispersal parameter of the storage vault; when the width dispersalparameter of the storage vault is to be increased, updating the originalplurality of storage units to include at least one additional storageunit to generate an updated plurality of storage units associated as anext generation of the storage vault; and storing the set of encodeddata slices in the updated plurality of storage units of the DSNassociated as the next generation of the storage vault.

In an embodiment, determining whether to increase the width dispersalparameter of the storage vault can include evaluating a performanceparameter of the storage vault and increasing the width dispersalparameter when the performance parameter of the storage vault comparesunfavorably to a performance threshold. Updating the original pluralityof storage units can include determining an updated width dispersalparameter of the storage vault based on a difference between theperformance parameter of the storage vault and the performancethreshold. Updating the original plurality of storage units can includedetermining an updated width dispersal parameter of the storage vault byincrementing a previous width dispersal parameter of the storage vaultby a predetermined number. Updating the original plurality of storageunits can include determining an updated width dispersal parameter ofthe storage vault and determining an incremental number of storage unitsbased on a difference between the updated width dispersal parameter anda previous width dispersal parameter of the storage vault. Updating theoriginal plurality of storage units can include determining anunutilized capacity for each of the original plurality of storage units.Updating the original plurality of storage units to include at least oneadditional storage unit can include selecting the at least oneadditional storage units from a set that includes at least one dormantstorage unit and at least one non-dormant storage unit having unutilizedcapacity.

The method described above in conjunction with the DST processing unit16 can alternatively be performed by other modules of a dispersedstorage network, of a dispersed storage and tracking network or by otherdevices. In addition, at least one memory section that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), cause the one or more computing devices to perform any or all ofthe method steps described above.

In a particular example, FIG. 43A illustrates an initial configurationwhere the previous storage units 1-4 are mapped to a first generation ofa storage vault such that storage unit 1 is associated with storingencoded data slices of slice index 1, storage unit 2 is associated withstoring encoded data slices of slice index 2, storage unit 3 isassociated with storing encoded data slices of slice index 3, andstorage unit4 is associated with storing encoded data slices of sliceindex 4.

First-generation sets of encoded data slices are stored in the storageunits 1-4 in accordance with the mapping. At a point in time, each ofthe storage units 1-4 has x bytes of unutilized storage capacity whentotal storage capacity of each of the storage units is substantially thesame. A determination may be made at the point in time to add a newgeneration to accommodate at least one of a wider pillar width andsimply adding storage capacity.

FIG. 43B illustrates the point in time of FIG. 43A when a new generationis to be added to accommodate a wider pillar width for the vault. As aspecific example, two new storage units (e.g., storage units 5-6) arecommissioned to support the new generation (e.g., generation 2), whereeach of the new storage units includes a minimum of x bytes of totalavailable storage capacity. A mapping of the new generation to theprevious storage units 1-4 and the two new storage units 5-6 includesassignment of generation 2 slice index 1 to storage unit 1 , slice index2 to storage unit 2, through slice index 6 to the storage unit 6.

Second sets of encoded data slices are stored in the storage units 1-6in accordance with the updated mapping. At another point in time, eachof the storage units 1-6 has y bytes of unutilized storage capacity. Adetermination may be made at the point in time to add another newgeneration to accommodate at least one of an even wider pillar width andfurther adding of storage capacity.

FIG. 43C illustrates the point in time of FIG. 43B when another newgeneration (e.g., generation 3) is to be added to accommodate an evenwider pillar width (e.g., 8) for the vault. As a specific example, twomore new storage units (e.g., storage units 7- 8) are commissioned tosupport generation 3, where each of the other new storage units includesa minimum of y bytes of total available storage capacity. A mapping ofthe new generation to the storage units 1-6 and the two more new storageunits 7-8 includes assignment of generation 3 slice index 1 to storageunit 1, slice index 2 to storage unit 2, through slice index 8 to thestorage unit 8.

FIG. 43D is a flowchart illustrating another example of commissioningstorage units. The method includes step 550 where a processing module(e.g., of a distributed storage and task (DST) client module) determineswhether to expand a width parameter of dispersal parameters of adispersed storage error coding function for data to be stored in astorage vault that utilizes a set of storage units. As a specificexample, the processing module detects an unfavorable comparison ofdesired performance (e.g., retrieval reliability) with actualperformance of the set of storage units. Alternatively, the processingmodule determines to add storage capacity without expanding the width ofdispersal parameter.

When expanding the pillar width, method continues at step 552 where theprocessing module determines an updated width. As a specific example,the processing module estimates the updated width based on a comparisonof actual performance to desired performance. As another specificexample, the processing module adds a default width expansion incrementto a current width to produce the updated width. When adding storagecapacity without expanding the width, the processing module indicates tocontinue utilizing the current width.

The method continues at step 554 where the processing module determinesan unutilized storage capacity level of each storage unit of the set ofstorage units. As a specific example, the processing module issues aquery and receives a response. The method continues at step 556 wherethe processing module determines an incremental number of storage unitsbased on the updated width and a previous width (e.g., current width).As a specific example, the processing module determines to utilize twoadditional storage units when the previous width is 4 and the updatedwith is 6. Alternatively, the processing module determines theincremental number of storage units based on storage capacityutilization when the storage units are to be added to expand storagecapacity. As a specific example, the processing module indicates toutilize two additional storage units when an estimated time frame beforeadding even further storage units is greater than a time frame thresholdlevel.

The method continues at step 558 where the processing module commissionsthe incremental number of storage units, where each storage unitincludes at least the unutilized storage capacity level amount ofunutilized storage capacity. As such, the set of storage units and theincremental storage units have substantially the same amount ofunutilized storage capacity. As a specific example, the processingmodule activates a dormant storage unit with sufficient unutilizedstorage capacity.

The method continues at step 560 where the processing module establishesan updated set of storage units that includes the set of storage unitsand the incremental number of storage units. As a specific example, theprocessing module identifies each storage unit and updates a storage settable in a sub-registry associated with the vault. The method continuesat step 562 where the processing module establishes a new generation forthe storage vault. As a specific example, the processing moduleincrements a previous generation number by one and updates thesub-registry associated with the vault.

The method continues at step 564 where the processing module associatesthe new generation with the updated set of storage units. As a specificexample, the processing module updates the sub-registry associated withthe vault to map the new generation to the updated set of storage units,where slice name ranges for each slice index of the updated width arespread out evenly amongst the updated set of storage units. When storingthe data to be stored, the method continues at step 566 where theprocessing module utilizes the new generation and the updated set ofstorage units. Alternatively, or in addition to, the processing modulemay push the updated sub-registry to entities of a dispersed storagenetwork associated with the set of storage units.

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16, the network 24, and the distributedstorage and task network (DSTN) module 22 of FIG. 1. The DSTN module 22includes a set of DST execution units 1-n of FIG. 1. Each DST executionunit includes the processing module 84 and the memory 88 of FIG. 3.

In an example of operation, the DST processing unit 16 issues, via thenetwork 24, requests to the DSTN module 22, and DST execution unitsissue responses, via the network 24, to the DST processing unit 16. Therequests include at least one of a write slice access request 600, aread slice access request 604, and a list slice request 606. Write sliceaccess request 600 includes one or more of a slice name field 601, aslice field 602, and a revision field 603. Slice name field 601 includesa slice name entry corresponding to a slice entry of slice field 602.Revision field 603 includes a revision entry corresponding to aparticular revision of the slice name for the slice entry. Read sliceaccess request 604 includes slice name field 605. List slice request 606includes a slice name range 607, where slice name range 607 includes astarting slice name and an ending slice name for a plurality of slicesassociated with slice names that fall within the slice name range.

The responses include at least one of a write slice access response 610that corresponds to the right slice access request, a read slice accessresponse 612 that corresponds to the read slice access request, and alist slice response 615 that corresponds to the list slice request.Write slice access response 610 includes a write status field 611 thatincludes one or more write status entries corresponding to a result(e.g., succeeded, failed) of processing a corresponding write sliceaccess request. Read slice access response 612 includes one or moreslice fields 613 and one or more corresponding revision fields 614.Slice field 613 includes a slice entry and revision field 614 includes arevision entry corresponding to a slice. Read slice access response 612may further include one or more of a slice revision count 617 and aslice length indicator 619. Slice revision count 617 indicates a numberof visible revisions responding to a common slice name. Slice lengthindicator 619 indicates a number of bytes of a corresponding slice.

List slice response 615 includes one or more of a slice name field 616and a revision field 618. Slice name field 616 indicates a slice name ofa slice storage in a DST execution unit, where the slice name fallswithin the slice name range of a corresponding list slice request.Revision field 618 includes a revision entry corresponding to a sliceassociated with the slice name. As a specific example, multiplerevisions are indicated for multiple slices that correspond to a commonslice name. List slice responses 615 may further include one or more ofthe slice revision count 617 and slice length indicator 619.

As a specific example of operation, the DST processing unit 16 issues,via the network 24, a set of write slice access requests 1-n to the setof DST execution units 1-n, where the set of write slice access requestincludes a set of slice names, a set of revision entries, and a set ofslices corresponding to the set of slice names and set of revisionentries. With the set of write slice access requests issued, the DSTexecution unit n receives a write slice access request n. The processingmodule 84 of the DST execution unit n stores the slice in the memory 88.The processing module 84 generates slice information 620 for the slice622 and stores the slice information in the memory 88. The sliceinformation includes one or more of storage location information of theslice, a slice name, a revision number (e.g., rev entry), a sliceintegrity value for the slice, and a slice length indicator for theslice. The storage location information includes one or more of a memorydevice identifier (ID) of a memory device utilized to store the slice, aoffset location within the memory device corresponding to where theslice is stored, and any other descriptions of the storage locationassociated with storing of the slice to enable subsequent retrieval ofthe slice. The processing module 84 issues, via the network 24, a writeslice access response n to the DST processing unit 16, where the writeslice access response n includes a write status (e.g., storagesucceeded) with regards to storage of the slice.

As another specific example of operation, the DST processing unit 16issues, via the network 24, a set of read slice access requests 1-n tothe set of DST execution units 1-n, where the set of read slice accessrequest includes the set of slice names. With the set of read sliceaccess requests issued, the DST execution unit n receives a read sliceaccess request n. The processing module 84 of the DST execution unit naccesses the memory 88 to recover the slice information corresponding tothe slice name. The processing module 84 interprets the sliceinformation to identify the storage location associated with theprevious storage of the slice. The processing module utilizes theinterpreted slice information to retrieve the slice from the memory 88.The processing module issues, via the network 24, a read slice accessresponse n to the DST processing unit 16, where the read slice accessresponse n includes the slice and the revision of the slice. Theoperation of the DST execution unit is discussed in greater detail withreference to FIG. 44B.

FIG. 44B is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit that includes theprocessing module 84 and the memory 88 of FIG. 44A. The processingmodule 84 includes a deterministic function module 628. The memory 88includes one or more memory devices, where data is stored as at leastone of location table information 630, slices 638 and 640, and a slicename tree 642.

In an example of operation, the processing module 84 receives the writeslice access request of FIG. 44A, processes the write slice accessrequest, and issues the write slice access response of FIG. 44A. As aspecific example of processing the write slice access request,deterministic function module 628 performs a corresponding deterministicfunction on one or more of the slice name 624 and the revision 626 ofthe write slice access request to produce index number 632. Thecorresponding deterministic function includes at least one of a hashingfunction, a hash-based message authentication code function, a maskgenerating function, and a sponge function. Having produced the indexnumber, the processing module 84 obtains (e.g., accesses an availablestorage location table, initiates a query, receives a response) anavailable storage location 634 for storage of the slice of the writeslice access request. The processing module 84 accesses the locationtable utilizing the index number to store the storage location. Thelocation table includes one or more of an index field 632, storagelocation field 634, and an associated information field 636. Index field632 includes an index entry corresponding to the index number (1-3 . . .). Storage location field 634 includes a storage location entrycorresponding to the storage location of the slice. Associatedinformation field 636 includes one or more associated informationentries (xxxx), where an associated information entry includes one ormore of the slice name of the write slice access request, the revisionnumber, the slice length of the revision of the slice, and an integrityvalue of the revision of the slice.

Having stored the storage location and/or associated information in anentry of the location table corresponding to the index number, theprocessing module 84 stores the slice in the memory 88 in accordancewith the storage location. Having stored the slice, the processingmodule 84 stores slice information as a new entry in slice name tree642. Slice name tree 642 includes a tree structure, where the treestructure includes linked pairings of slice names/revisions and theassociated information of the slice name and revision. Each pairing isdirectly associated with at most two other pairings, where a firstpairing is a next higher slice name/revision and a second pairing is anext lower slice name/revision. As a specific example, the processingmodule 84 identifies a location within the slice name tree for insertionof the new entry based on slice name 644 and revision of the write sliceaccess request 646 and indicated slice names 648, 652, 656, 660, 664,668 and respective revisions 650, 654, 658, 662, 666, and 670 ofcurrently stored pairings within the slice name tree. With the locationwith the slice name tree for insertion identified, the processing module84 generates the new entry and stores the new entry in the slice nametree, where the storing includes linking the new entry to one or moreother previous entries of the slice name tree.

In another example of operation, the processing module 84 receives theread slice access request of FIG. 44A, processes the read slice accessrequest, and issues the read slice access response of FIG. 44A. As aspecific example of processing the read slice access request, thedeterministic function module performs the corresponding deterministicfunction on one or more of the slice name and an estimated revision ofthe read slice access request to reproduce the index number. Theprocessing module 84 may perform the deterministic function for aplurality of estimated revisions. Having produced the index number, theprocessing module 84 accesses the location table utilizing the indexnumber to retrieve the storage location and/or associated information.The processing module utilizes the storage location to retrieve theslice from the memory 88. The processing module 84 issues the read sliceaccess response that includes the slice.

As another specific example of processing the read slice access request,the processing module 84 accesses the slice name tree using the slicename of the read slice request to obtain the associated information.Having obtained associated information, the processing module 84identifies one or more revisions associated with the slice of the slicename. For each revision of the one or more revisions, the deterministicfunction module performs the corresponding deterministic function on theslice name and the revision to reproduce the index number. Havingreproduced the index number, the processing module 84 accesses thelocation table to retrieve the storage location. Having retrieved thestorage location, the processing module 84 retrieves the slice andissues the read slice access response that includes the slice.

In another example of operation, the processing module 84 receives thelist slice request of FIG. 44A, processes the list slice request, andissues the list slice response of FIG. 44A. as a specific example ofprocessing the list slice request, the processing module 84 accesses theslice name tree utilizing the starting slice name and the ending slicename of the list slice request to retrieve slice name/revisions andassociated information. The processing module 84 issues the list sliceresponse to include the retrieved slice name/revisions and theassociated information. While not shown, the list response is aspreviously shown in FIG. 44A, element 615.

FIG. 44C is a flowchart illustrating an example of accessing encodeddata slices. The method includes step 672 where a processing module(e.g., of a distributed storage and task (DST) execution unit) receivesa slice access request that includes a slice name. The method continuesat step 674 where the processing module obtains one or more revisionnumbers for the slice access request. As a specific example, theprocessing module extracts a revision number from the slice accessrequest. As another specific example, the processing module searches aslice name tree to extract the one or more revision numbers.

For each combination of revision number and the slice name, the methodcontinues at step 676 where the processing module performs adeterministic function on the combination to produce a slice locationtable index value. The method continues at step 678 where the processingmodule accesses a slice location table utilizing the slice locationtable index value to obtain a slice location. The method continues atstep 680 where the processing module accesses a slice utilizing theslice location. As a specific example, the processing module stores theslice at the slice location when the slice access request includes awrite slice request. As another specific example, the processing moduleretrieves the slice from the slice location when the slice accessrequest includes a read slice request. The method continues at step 682where the processing module generates a slice access response based onthe accessing of the slice. As a specific example, the processing modulegenerates a write slice access response when the slice access requestincludes the write slice access request. As another specific example,the processing module generates a read slice access response when theslice access request includes the read slice access request. The methodcontinues at step 684 where the processing module sends the slice accessresponse to a requesting entity.

FIG. 44D is a flowchart illustrating an example of listing encoded dataslices. The method includes step 686 where a processing module (e.g., ofa distributed storage and task (DST) execution unit) receives a listslice request that includes a slice name range. The method continues atstep 688 where the processing module accesses a slice name tree usingthe slice name range to obtain associated information for one or moreslices of the slice name range. As a specific example, the processingmodule traverses the slice name tree to find each revision of each slicename within the slice name range and, for each revision, the processingmodule extracts associated information from the slice name tree.

The method continues at step 690 where the processing module generates alist slice responses to include the associated information. As aspecific example, the processing module generates the list sliceresponses to include, for each slice name, a revision number for eachrevision, a slice length for each revision, and a slice integrity valuefor each revision. The method continues at step 692 where the processingmodule sends the list slice response to a requesting entity.

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16, the network 24, and the distributedstorage and task network (DSTN) module 22 of FIG. 1. The DSTN module 22includes a set of DST execution units 1-n of FIG. 1. Each DST executionunit includes the processing module 84 and the memory 88 of FIG. 3.

In an example of operation, the DST processing unit 16 issues a set ofread slice requests 700 to the set of DST execution units 36. A DSTexecution unit 36 receives a corresponding read slice request andidentifies a session associated with the read slice request. As aspecific example, the processing module 84 of the DST execution unit 36interprets a transaction number of the read slice request to identifythe session. As another specific example, the processing module 84initiates a query and receives a response that includes a sessionidentifier including the identity of the session. As yet anotherexample, the processing module 84 receives the session identifier fromat least one other DST execution unit 36.

Having identified the session, the processing module 84 determines oneor more tasks associated with the read slice request. As a specificexample, the processing module performs a lookup for tasks associatedwith requests. Having determined the one or more tasks, the processingmodule 84 queues the one or more tasks by storing the one or more tasksin a task queue 706 of the memory 88. While the one or more tasks arequeued, the processing module 84 determines whether the session is stillactive. As a specific example, the processing module 84 indicates thatthe session has ended when receiving an end session message 704indicating that the session has ended. As another specific example, theprocessing module indicates that the session has ended when a timeframehas expired since receiving the read slice requests. As yet anotherexample, the processing module 84 indicates that the session has endedwhen detecting disconnection, via the network 24, with the DSTprocessing unit 16. As another specific example, the processing moduleindicates that the session has ended when receiving a resource indicatorindicating that resources are not available to complete the task. Asanother specific example, the processing module indicates that thesession has ended when receiving an indication that a security threat isavailable. As another specific example, the processing module indicatesthat the session has ended when receiving an indication that anaggregated partial result threshold has been met (high confidence thatenough partial results have been collected (aggregated) to conclude asuccessful retrieval. As another specific example, the processing moduleindicates that the session has ended when initiating a query to make thedetermination. As another specific example, the processing moduleindicates that the session has ended when performing a look-up whichindicates a session termination. As another specific example, theprocessing module indicates that the session has ended whende-prioritizing another task associated with the resources.

When the processing module 84 detects that the session is not active,the processing module 84 cancels the one or more tasks associated withthe session prior to execution. As a specific example, the processingmodule 84 removes the one or more tasks from the task queue.Alternatively, the processing module 84 retrieves the one or more tasksfrom the task queue and facilitates execution of the one or more taskswhen resources become available. As a specific example, the processingmodule 84 retrieves a slice from the memory 88 and issues, via thenetwork 24, a read slice response 702 to the DST processing unit 16,where the read slice response includes the slice.

FIG. 45B is a flowchart illustrating an example of canceling a request.The method includes step 708 where a processing module (e.g., of adistributed storage and task (DST) execution unit) receives a requestwhere the request requires resources (e.g., processing resources,storage resources, communication resources) of the DST execution unit.The method continues at step 710 where the processing module identifiesa session associated with the request. As a specific example, theprocessing module assigns a new session number. The method continues atstep 712 where the processing module queues one or more tasks associatedwith the request. As a specific example, the processing moduleidentifies the one or more tasks based on the request and stores the oneor more tasks in a task queue of a memory of the DST execution unit.

While the one or more tasks are still queued, the method continues atstep 714 where the processing module determines whether the session isstill active. As a specific example, the processing module determinesthat the session is still active when not detecting that the session isinactive. As a specific example of detecting that the session is anactive session, the processing module receives an end of sessionmessage. The method branches to step 718 where the processing moduledetermines whether resources are available when the session is stillactive. The method continues to next step 716 when the session is notactive. The method continues at step 716 where the processing modulecancels the one or more tasks associated with the session when thesession is not still active. As a specific example, the processingmodule removes the one or more tasks from the task queue.

When the session is still active, the method continues at step 718 wherethe processing module determines whether resources are available toexecute the one or more tasks. As a specific example, the processingmodule receives a resource indicator indicating that resources areavailable. As another specific example, the processing module initiatesa resource query and receives a resource response. As yet anotherspecific example, the processing module de-prioritizes another taskassociated with the resources. The method branches to step 720 where theprocessing module executes the one or more tasks when the resources areavailable. The method loops back to the step where the processing moduledetermines whether the session is still active when the processingmodule determines that the resources are not available. When theresources are available, the method continues at the step where theprocessing module executes the one or more tasks utilizing the availableresources.

FIG. 45C is a flowchart illustrating an example of canceling a request.The method includes step 722 where a processing module (e.g., of adistributed storage and task (DST) execution unit) receives a read/write(R/W) request (sets of encoded data slices) where the R/W requestrequires resources (e.g., processing resources, storage resources,communication resources) of the DST execution unit. The method continuesat step 724 where the processing module identifies a decode thresholdassociated with the R/W request. As a specific example, the pillar widthis 5 and the decode threshold is 3. The method continues at step 726where the processing module queues one or more tasks associated with therequest. As a specific example, the processing module identifies the oneor more tasks based on the request and stores the one or more tasks intask queue 706 of a memory of the DST execution unit.

While the one or more tasks are still queued and the R/W is in progress(encoded data slices being retrieved/written to memory within the DSN),the method continues at step 728 where the processing module determineswhether the decode threshold is met. As a specific example, theprocessing module determines that the 3 pillars have successfullyretrieved/stored the encoded data slices of the R/W request. Once thedecode threshold is met, the method continues at step 730 where theprocessing module cancels retrieval/storage of additional encoded dataslices and at step 731 sends a message to units which have yet tocomplete the R/W request to cancel the request. This cancellationmessage may reduce network congestion and wasted retrieval/storagecycles.

When a successful decode threshold has been met, the method continues atstep 732 where the processing module determines whether resources areavailable to execute the one or more tasks. As a specific example, theprocessing module receives a resource indicator indicating thatresources are available. As another specific example, the processingmodule initiates a resource query and receives a resource response. Asyet another specific example, the processing module de-prioritizesanother task associated with the resources. When the resources are notavailable, the method loops back to the step where the processing moduledetermines whether the resources are available. When the resources areavailable, the method continues at the step 734 where the processingmodule executes the one or more tasks utilizing the available resources.

FIGS. 46A-D is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distribute storage andtask (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, andthe distributed storage and task network (DSTN) module 22 of FIG. 1. TheDSTN module 22 includes a set of DST execution (EX) units 1-5.Alternatively, the set of DST execution units may include any number ofDST execution units. Hereafter, the DST processing unit 16 may beinterchangeably referred to as a computing device. Hereafter, a DSTexecution unit may be interchangeably referred to as an execution unitand the set of DST execution units may be interchangeably referred to asa set of storage units. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1.

The DSN functions to adjust tasks 740 executed by the set of DSTexecution units. The tasks 740 includes data access tasks anddistributed computing tasks. In an example of operation, the DSTprocessing unit 16 receives tasks 740, generates a plurality of taskrequests, and sends, via the network 24, corresponding sets of sub-taskrequests to the set of execution units, where a task request includes aset of sub-task requests. Hereafter, the plurality of task requests maybe referred to interchangeably as partial tasks 742. For example, theDST processing unit 16 generates the partial tasks 742 to includepartial tasks 1-5, where each set of sub-task requests includes apartial task 1, a partial task 2, a partial task 3, a partial task 4,and a partial task 5. For instance, the DST processing unit 16 generatesa second set of sub-task requests to include a request 2_1, a request2_2, a request 2_3, a request 2_4, and a request 2_5; a third set ofsub-task requests to include a request 3_1, a request 3_2, a request3_3, a request 3_4, and a request 3_5; and a fourth set of sub-taskrequests to include a request 4_1, a request 4_2, a request 4_3, arequest 4_4, and a request 4_5.

FIG. 46A illustrates further steps of the example of operation of theadjusting of the tasks where the set of DST execution units receives,over time, a plurality of sets of sub-task requests from the DSTprocessing unit 16. Having received the plurality of sets of sub-taskrequest, the set of DST execution units stores the corresponding sets ofsub-task requests, where, in an example, each of a first, a second, anda third execution unit of the set of execution unit stores,respectively, a first, a second, and a third sub-task request of each ofthe corresponding sets of sub-task requests to produce, respectively, afirst, a second, and a third plurality of sub-task requests. As aspecific example, the first, the second, and the third execution unitsrespectively stores the first, the second, and the third plurality ofsub-task requests in first, second, and third holding queues. Forinstance, DST execution unit 1 stores the first plurality of sub-taskrequests to include storing request 2_1, request 3_1, and request 4_1 ina holding queue 1 of the DST execution unit 1.

FIG. 46B illustrates further steps of the example of operation of theadjusting of the tasks where the set of DST execution units generatestask status information 744 for the pluralities of sub-task requests andshare, via the network 24, the generated task status information 744.Hereafter, the task status information 744 may be interchangeablyreferred to as sub-task estimation data. The task status information 744includes one or more of an execution ready status, an execution notready status, a level of execution readiness, required resources forsub-task execution, available resources for sub-task execution, aprocessing capability level, storage capability level estimatedtimeframe for the initiation of sub-task execution, estimated durationfor sub-task execution, and sub-task sequencing information.

As a specific example, the first, the second, and the third executionunits respectively generating first, second, and third sub-taskestimation data for the first, the second, and the third plurality ofsub-task requests. For instance, the first execution unit determines anexecution ready status for a first selected sub-task request (e.g.,request 2_1) of the first plurality of sub-task requests when the firstexecution unit has resources available to process the first selectedsub-task request, or the first execution unit determines an executionnot ready status for the first selected sub-task request of the firstplurality of sub-task requests when the first execution unit does nothave the resources available to process the first selected sub-taskrequest. As another instance, the second execution unit determines anexecution ready status for a second selected sub-task request (e.g.,request 2_2) of the second plurality of sub-task requests when thesecond execution unit has resources available to process the secondselected sub-task request or the second execution unit determines anexecution not ready status for the second selected sub-task requests ofthe second plurality of sub-task requests when the second execution unitdoes not have the resources available to process the second selectedsub-task request.

As yet another instance, the third execution unit determines anexecution ready status for a third selected sub-task request (e.g.,request 2_3) of the third plurality of sub-task requests when the thirdexecution unit has resources available to process the third selectedsub-task request for the third execution unit determines an executionnot ready status for the third selected sub-task requests of the thirdplurality of sub-task requests when the third execution unit does nothave the resources available to process the third selected sub-taskrequest. As a still further instance, a fourth execution unit determinesan execution not ready status for a fourth selected sub-task request(e.g., request 2_4) and an execution ready status for another fourthselected sub-task request (e.g., request 3_4) of another set of sub-taskrequests.

FIG. 46C illustrates further steps of the example of operation of theadjusting of the tasks where the set of DST execution units adjuststiming, sequencing, or processing of the plurality of sub-task requestsbased on the task status information 744 to produce a pluralities ofpartial results, where due to one or more difference factors from a listof difference factors, the execution units process the pluralities ofsub-task requests at difference paces. The list of difference factorsincludes differences in amounts of data to be processed per sub-taskrequest, processing capabilities, memory storage capabilities, andnetworking capabilities.

As a specific example, the first, the second, and the third executionunits respectively adjust the timing, the sequencing, or the processingof the first, the second, and the third plurality of sub-task requestsbased on the first, the second, and the third sub-task estimation datato produce a first, a second, and a third plurality of partial results.For instance, the DST execution unit 4 interprets the task statusinformation from DST execution units 1-3 to determine that each of theDST execution units 1-3 is ready to execute corresponding sub-tasks ofthe second set of sub-tasks. Having determined that the DST executionunits 1-3 are ready to execute the corresponding sub-tasks of the secondset of sub-tasks, the DST execution unit 4 selects request 2_4 andadjusts sequencing of the fourth plurality of sub-task requests toprovide available resources for execution of request 2_4.

FIG. 46D illustrates further steps of the example of operation of theadjusting of the tasks where the set of DST execution units, havingadjusted the plurality of sub-task requests, transfers selected sub-taskrequests from the holding queues to execution queues prior to executingthe selected sub-task requests. As a specific example, the first, thesecond, the third, and the fourth DST execution unit respectivelytransfer first, second, and third selected sub-task requests of thefirst, the second, the third, and the fourth plurality of sub-taskrequests from the first, the second, the third holding, and the fourthqueues to first, second, third, and fourth execution queues prior torespectively executing the first, the second, the third, and the fourthselected sub-task requests.

Having transferred the selected sub-task requests, the execution unitsexecute the transferred and selected sub-task requests to produce thefirst, the second, the third, and the fourth plurality of partialresults. For instance, the DST execution unit 4 executes the request 2_4to retrieve an encoded data slice and generate a partial result 2_4 thatincludes the retrieved encoded data slice. As another instance, the DSTexecution unit 4 executes the request 2_4 to perform a distributedcomputing function to generate the partial result 2_4.

Having produced the pluralities of partial results, the execution unitssend the pluralities of partial results to the DST processing unit 16.As a specific example, DST execution units 1- 4 send, via the network24, partial results 2_1, 2_2, 2_3, and 2_4 to the DST processing unit16. The DST processing unit 16 receives the pluralities of partialresults and compiles the pluralities of partial results to produce aplurality of results 746. As a specific example, the DST processing unit16 receives the partial results partial results 2_1, 2_2, 2_3, and 2_4and compiles the partial results partial results 2_1, 2_2, 2_3, and 2_4to produce the result 746.

Alternatively, when a quorum of the first, the second, and the thirdexecution units exists, where a quorum is reached when two of the first,the second, and the third selected sub-task requests have the executionready status, the quorum of execution units executes two of the first,the second, or the third selected sub-task requests to produce two of afirst, a second, and a third partial results of the first, the second,and the third plurality of partial results. For instance, DST executionunits 1-4 forms the quorum when the requests 2_1 through 2_4 are readyfor execution and executes the requests 2_1 through 2_4 to produce thepartial results 2_1 through 2_4.

Alternatively, or in addition to, for the first, the second, or thethird execution unit not in the quorum of execution units, the executionunit not in the quorum deletes the respective first, the second, and thethird selected sub-task requests when the two of the first, the second,and the third partial results have been produced and the remaining oneof the first, the second, and the third selected sub-task requests hasthe execution not ready status. For instance, DST execution unit 5deletes request 2_5 when the quorum including DST execution units 1-4executes the requests 2_1 through 2_4 to produce the partial results 2_1through 2_4.

FIG. 46E is a flowchart illustrating an example of adjusting executionof tasks. In particular, a method is presented for use in conjunctionwith one or more functions and features described in conjunction withFIGS. 1-39, 46 A-D, and also FIG. 46E. The method continues at step 750where a processing module of a computing device of a dispersed storagenetwork (DSN) issues a plurality of sets of sub-task requests to a setof execution units of the DSN. For example, the computing devicegenerates a plurality of task requests, where a task request of theplurality of task requests includes a set of sub-task requests and wherethe computing device sends corresponding sets of sub-task requests tothe set of execution units when task requests of the plurality of taskrequests are generated.

The method continues at step 752 where, the set of execution units, overtime, receive the plurality of sets of sub-task requests. The methodcontinues at step 754 where the set of execution unit stores thecorresponding sets of sub-task requests. Each of a first, a second, anda third execution unit of the set of execution unit stores,respectively, a first, a second, and a third sub-task request of each ofthe corresponding sets of sub-task requests to produce, respectively, afirst, a second, and a third plurality of sub-task requests. Forexample, the first, the second, and the third execution unitsrespectively stores the first, the second, and the third plurality ofsub-task requests in first, second, and third holding queues.

The method continues at step 756 where each execution unit generatessub-task estimation data for a plurality of sub-task requests. Forexample, the first, the second, and the third execution unitsrespectively generate first, second, and third sub-task estimation datafor the first, the second, and the third plurality of sub-task requests.As a specific example of the generating of the sub-task estimation data,the first execution unit determines an execution ready status for afirst selected sub-task request of the first plurality of sub-taskrequests when the first execution unit has resources available toprocess the first selected sub-task request or the first execution unitdetermines an execution not ready status for the first selected sub-taskrequest of the first plurality of sub-task requests when the firstexecution unit does not have the resources available to process thefirst selected sub-task request. Similarly, each of the second executionunit and the third execution unit determines an execution ready statusor an execution not ready status for a respective second and thirdselected sub-task request based on respective resource availability.

The method continues at step 758 where each execution unit adjusts theplurality of sub-task requests based on sub-task estimation data of theset of execution units. For example, the first, the second, and thethird execution units respectively adjust timing, sequencing, orprocessing of the first, the second, and the third plurality of sub-taskrequests based on the first, the second, and the third sub-taskestimation data to produce a first, a second, and a third plurality ofpartial results, where, due to one or more difference factors from alist of difference factors, the first, the second, and the thirdexecution units process the first, the second, and the third pluralityof sub-task requests at difference paces. The list of difference factorsincludes differences in amounts of data to be processed per sub-taskrequest, processing capabilities, memory storage capabilities, andnetworking capabilities. The first, the second, and the third executionunits respectively transfer first, second, and third selected sub-taskrequests of the first, the second, and the third plurality of sub-taskrequests from the first, the second, and the third holding queues tofirst, second, and third execution queues prior to respectivelyexecuting the first, the second, and the third selected sub-taskrequests.

The method continues at step 760 where a quorum of execution unitsexecutes selected sub-task requests to produce partial results. Forexample, when a quorum of the first, the second, and the third executionunits exists, where a quorum is reached when two of the first, thesecond, and the third selected sub-task requests have the executionready status, the quorum of execution units executes two of the first,the second, or the third selected sub-task requests to produce two of afirst, a second, and a third partial results of the first, the second,and the third plurality of partial results. Alternatively, or inaddition to, for the first, the second, or the third execution unit notin the quorum of execution units, the execution unit not in the quorumdeletes the respective first, the second, and the third selectedsub-task requests when the two of the first, the second, and the thirdpartial results have been produced and the remaining one of the first,the second, and the third selected sub-task requests has the executionnot ready status.

The method continues at step 762 where the quorum of execution unitssends the partial results to the computing device for compiling toproduce a plurality results. For example, the first, the second, and thethird execution unit send the first, the second, and the third pluralityof partial results to the computing device, where the computing devicecompiles the first, the second, and the third plurality of partialresults to produce a plurality of results.

The method described above in conjunction with the computing device andthe set of execution units can alternatively be performed by othermodules of the dispersed storage network or by other devices. Inaddition, at least one memory section that stores operationalinstructions can, when executed by one or more processing modules of oneor more computing devices of the dispersed storage network (DSN), causethe one or more computing devices to perform any or all of the methodsteps described above.

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a scheduling unit 768, andthe distributed storage and task (DST) processing unit 16, the network24, and the distributed storage and task network (DSTN) module 22 ofFIG. 1. The DSTN module 22 includes a set of DST execution units 1-n ofFIG. 1.

In an example of operation, the DST processing unit 16 issues a set ofrequests 770 (e.g., for a write sequence, for a read sequence) to theDSTN module 22. A DST execution unit 36 receives a request of the set ofrequests 770. Having received the request, the DST execution unit 36issues status information 772 to the scheduling unit 768. The statusinformation 772 includes one or more of the request, a common sessionidentifier (ID), a timestamp of receipt of the request, a queuing statusindicator (e.g., estimated time of de-queuing, queue depth, queuepriority), and an estimated time of execution of the request.

The scheduling unit 768 receives the status information 772 from the DSTexecution unit and from one or more of the other DST execution units 36in response to requests received by the other DST execution units 36.Having received the status information 772, the scheduling unit 768aggregates the status information 772 for one or more requestsassociated with the common session of a plurality of simultaneouslyactive sessions. Having aggregated the status information 772, thescheduling unit 768 generates scheduling information 774, where thescheduling information 774 includes one or more of the task queuing andexecution status for the common session, a recommended timeframe forexecution of one or more tasks associated with the common session, andtask queuing and execution status for other sessions. As a specificexample, the scheduling unit 768 generates the scheduling information774 such that associated requests (e.g., similar requests) of the commonsession are executed at substantially the same time by the set of DSTexecution units. Having generated the scheduling information 774, thescheduling unit 768 sends the scheduling information 774 to the set ofDST execution units 36. Each DST execution unit 36 utilizes thescheduling information 774 when prioritizing one or more tasks forexecution, where the one or more tasks are associated with the commonsession.

FIG. 47B is a flowchart illustrating an example of processing a request.The method continues at step 776 where an entity of a dispersed storagenetwork (DSN) receives a request associated with a session. As aspecific example, the entity of the DSN obtains a session identifier ofthe session based on the request. The method continues at step 778 wherethe entity of the DSN issues status information for the request to ascheduling unit. As a specific example, the entity of the DSN generatesthe status information to include a request identifier, a common sessionidentifier, a timestamp of receipt of the request, a queuing statusindicator, an estimated time of execution, a required resourceindicator, and a required resource availability level indicator.

The method continues at step 780 where the scheduling unit interpretsthe status information and other status information from one or moreother entities of the DSN to produce summarized status information. As aspecific example, the scheduling unit filters the status information toidentify a common session, identifies critical resources required forthe common session, identifies timing of critical resource availability,and generates a suggested task execution schedule for the entities ofthe DSN associated with the common session. The method continues at step782 where the scheduling unit sends the summarized status information toa plurality of entities of the DSN associated with a common session. Themethod continues at step 784 where the entity of the DSN executes therequest associated with the common session in accordance with thesummarized status information. As a specific example, the entity of theDSN queues the request in a prioritized order in accordance with thesummary status information to align execution of tasks of the requestwith availability resources and with parallel execution of similar tasksby other entities of the DSN.

FIGS. 48A-B is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and the distributed storage and task network (DSTN) module 22 of FIG. 1.The DSTN module 22 includes a plurality of DST execution units. Inparticular, the DSTN module 22 includes at least one set of DSTexecution units 1-n. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, a DSTexecution unit may be interchangeably referred to as a storage unit andthe set of DST execution units may be interchangeably referred to as aset of storage units.

Each DST execution unit includes the processing module 84 of FIG. 3, apending request memory 790, and a plurality of memories 1, 2, 3, etc.Each memory of the plurality of memories may be implemented utilizing atleast one of a disk drive memory, a solid state memory, and a tape drivememory. The pending request memory 790 may be implemented utilizing atleast one of the disk drive memory, the solid state memory, and the tapedrive memory. For example, each DST execution unit includes disk drivememories 1, 2, 3, etc. and a fast access solid state memory to implementthe pending request memory 790.

The DSN functions to access a plurality of sets of encoded data slicesin the set of DST execution units and to migrate stored encoded dataslices from one memory to another memory. In an example of operation ofaccessing the plurality of sets of encoded data slices, the DSTprocessing unit 16 receives a data access request 792. The data accessrequest 792 includes at least one of a store data request and a retrievedata request. Having received the data access request 792, the DSTprocessing unit 16 issues, via the network 24, a slice access requests1-n to the set of DST execution units 1-n. Issuing the slice accessrequests 1-n includes sending, via the network 24, slice access requests1 to the DST execution unit 1, slice access requests 2 to the DSTexecution unit 2, etc. Each slice access request includes at least oneof a write slice request, a read slice request, a list slice request,and a delete slice request. For example, the slice access requestincludes the write slice request when the data access request 792includes the store data request and the slice access request includesthe read slice request when the data access request 792 includes theretrieve data request.

Each DST execution unit receives a corresponding slice access requestand a processing module 84 of each DST execution unit stores thereceived slice access request in the pending request memory 790. Theprocessing module 84 retrieves the slice access request from the pendingrequest memory 790 in accordance with a performance approach. Theperformance approach may be based on one or more of an input/output (IO)rate of a memory that corresponds to a slice name of the slice accessrequest, a desired maximum IO rate, a timestamp associated with theslice access request, and a priority level associated with the sliceaccess request. For example, the processing module 84 retrieves a sliceaccess request from the pending request memory 790 that is an oldestrequest of requests associated with the memory 1, when the IO rate ofthe memory 1 is less than the desired maximum IO rate where theprocessing module 84 waits before retrieving the slice access requestwhen the IO of the memory 1 is greater than the desired maximum IO rate.

When retrieving the slice access request from the pending request memory790, the processing module 84 processes the slice access request toproduce a slice access response. Each slice access response includes atleast one of a write slice response, a read slice response, a list sliceresponse, and a delete slice response. For example, DST execution unit 3receives a slice access request 3 that includes a read slice request forencoded data slices A2_1 through A2_M. The processing module 84 of theDST execution unit 3 processes the slice access request 3 by determiningwhich memory (e.g., memory 1) of the memories is associated with storageof the encoded data slices A2_1 through A2_M, retrieving the encodeddata slices A2_1 through A2_M from the determined memory, and issuing,via the network 24, a slice access response 3 to the DST processing unit16, where the slice access response 3 includes a read slice responsethat includes the encoded data slices A2_1 through A2_M.

FIG. 48A illustrates initial steps of an example of operation of themigration of the stored encoded data slices from the one memory to theother memory where the processing module 84 monitors input/output (IO)rates of the plurality of disk drives (e.g., memories 1, 2, 3 etc.),where the storage unit stores a plurality of encoded data slices fromthe plurality of sets of encoded data slices in at least some of theplurality of disk drives, and where access requests for encoded dataslices of the plurality of encoded data slices occur at varying rates.

Having monitored the IO rates of the plurality of disk drives, theprocessing module 84 determine that the IO rate of a disk drive of theplurality of disk drives is exceeding the desired maximum IO rate. Forexample, the processing module 84 of the DST execution unit 3 determinesthat the IO rate of memory 1 is exceeding the desired maximum IO rate.

Having determined that the IO rate of the disk drive is exceeding thedesired maximum IO rate, the processing module 84 identifies a pendingaccess request (e.g., stored in the pending request memory 790) for anencoded data slice stored in the disk drive, where the encoded dataslice is of a set of encoded data slices of the plurality of sets ofencoded data slices and where a decode threshold number of encoded dataslices of the set of encoded data slices is needed to reconstruct a datasegment. As a specific example, the processing module 84 identifies thepending access request by one or more of analyzing historical accessrequest information regarding similar access requests to the pendingaccess request and determining a likelihood of future similar accessrequests to the pending access request. As another specific example, theprocessing module 84 identifies the pending access request bydetermining that multiple similar access requests to the pending accessrequest are pending in an access request queue of the pending requestmemory 790. For instance, the processing module 84 identifies multiplesimilar access requests for the encoded data slices A2_1 through A2_M.

FIG. 48B illustrates further steps of the example of operation of themigration of the stored encoded data slices from the one memory to theother memory where the processing module 84 evaluates disk driveprocessing rates of other storage units that are storing other encodeddata slices of the set of encoded data slices to determine whether theencoded data slice is needed to satisfy the pending access request. As aspecific example, the processing module 84 evaluates the disk driveprocessing rates of the other storage units by receiving, via thenetwork 24 from each of the other storage units, processing informationthat includes disk drive processing rates for each of a plurality ofother disk drives. For instance, the processing module 84 sends, via thenetwork 24, requests for the disk drive processing rates to the otherstorage units, where one of the requests includes identity of encodeddata slice or identity of one of the other encoded data slices. Theprocessing module 84 receives processing information 1 from DSTexecution unit 1, processing information 2 from DST execution unit 2 ,etc.

Having received the disk drive processing rates, the processing module84 identifies, for each of the other storage units, the disk driveprocessing rate of another disk drive of the plurality of other diskdrives storing one of the other encoded data slices. For example, theprocessing module 84 identifies the disk drive processing rate for thememory 1 from each of the other DST execution units when each of thememory 1 associated with storage of the other encoded data slices.Having identified the disk drives processing rate of the other diskdrive, the processing module 84 determines whether the decode thresholdnumber of the disk drive processing rate of the other disk drives havean IO rate below the desired maximum IO rate. For example, theprocessing module 84 determines whether ten other DST execution unitshave the IO rate below the desired maximum IO rate for the memory 1 ofeach of the ten other DST execution units when the decode thresholdnumber is ten.

When less than the decode threshold number of the disk drive processingrate of the other disk drives have an IO rate below the desired maximumIO rate, the processing module 84 indicates that the encoded data sliceis needed. When the decode threshold number of the disk drive processingrate of the other disk drives have an IO rate below the desired maximumIO rate, the processing module 84 indicates that the encoded data sliceis not needed.

When the encoded data slice is needed, the processing module 84 migratesthe encoded data slice to another disk drive of the plurality of diskdrives. For example, the processing module 84 migrates the encoded dataslices A2_1 through A2_M from memory 1 to memory 2. The migrating mayfurther include analyzing the IO rate of the memory 2 to determinewhether the memory 2 has available IO capacity. Having migrated theencoded data slices A2_1 through A2_M from memory 1 to memory 2, theprocessing module 84 deletes the encoded data slices A2_1 through A2_Mfrom memory 1 and associates the encoded data slices A2_1 through A2_Mwith memory 2 (e.g., updates a slice location table).

When the encoded data slice is not needed, the processing module 84deletes, from the pending request memory 790, the pending access requestfor the encoded data slice. For example, the processing module 84deletes pending access requests for the encoded data slices A2_1 throughA2_M.

FIG. 48C is a flowchart illustrating an example of migrating encodeddata slices. In particular, a method is presented for use in conjunctionwith one or more functions and features described in conjunction withFIGS. 1-39, 48 A-B, and also FIG. 48C. The method continues at step 800where a processing module of a storage unit of a dispersed storagenetwork (DSN) monitors input/output (IO) rates of a plurality of diskdrives, where the storage unit stores a plurality of encoded data slicesfrom a plurality of sets of encoded data slices in at least some of theplurality of disk drives, and where access requests for encoded dataslices of the plurality of encoded data slices occur at varying rates.

The method continues at step 802 where the processing module determinesthat the IO rate of a disk drive of the plurality of disk drives isexceeding a desired maximum IO rate. The method continues at step 804where the processing module identifies a pending access request for anencoded data slice stored in the disk drive, where the encoded dataslice is of a set of encoded data slices of the plurality of sets ofencoded data slices and wherein a decode threshold number of encodeddata slices of the set of encoded data slices is needed to reconstruct adata segment. The identifying the pending access request includes one ormore of analyzing historical access request information regardingsimilar access requests to the pending access request and determining alikelihood of future similar access requests to the pending accessrequest. The identifying the pending access request may further includedetermining that multiple similar access requests to the pending accessrequest are pending in an access request queue.

The method continues at step 806 where the processing module evaluatesdisk drive processing rates of other storage units that are storingother encoded data slices of the set of encoded data slices to determinewhether the encoded data slice is needed to satisfy the pending accessrequest. As a specific example, the processing module sends requests forthe disk drive processing rates to the other storage units, where one ofthe requests includes identity of the encoded data slice or identity ofone of the other encoded data slices. The processing module receives,from each of the other storage units, disk drive processing rates foreach of a plurality of other disk drives, and identifies, for each ofthe other storage units, the disk drive processing rate of another diskdrive of the plurality of other disk drives storing one of the otherencoded data slices. Having identified the disk drive processing rate ofthe other disk drive, the processing module determines whether thedecode threshold number of the disk drive processing rate of the otherdisk drives have an IO rate below the desired maximum IO rate.

When less than the decode threshold number of the disk drive processingrate of the other disk drives have an IO rate below the desired maximumIO rate, the processing module indicates that the encoded data slice isneeded. When the decode threshold number of the disk drive processingrate of the other disk drives have an IO rate below the desired maximumIO rate, the processing module indicates that the encoded data slice isnot needed. When the encoded data slice is not needed, the methodbranches to step 810. When encoded data slice is needed, the methodcontinues to step 808. When the encoded data slice is needed, the methodcontinues at step 808 where the processing module migrates the encodeddata slice to another disk drive of the plurality of disk drives. Whenthe encoded data slice is not needed, the method continues at step 810where the processing module deletes the pending access request for theencoded data slice.

The method described above in conjunction with a processing module canalternatively be performed by other modules of a dispersed storagenetwork or by other devices. In addition, at least one memory sectionthat stores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices of a dispersedstorage network (DSN), cause the one or more computing devices toperform any or all of the method steps described above.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: receiving, over time and by a set ofexecution units of the DSN, a plurality of sets of sub-task requestsfrom a computing device of the DSN, wherein the computing devicegenerates a plurality of task requests, wherein a task request of theplurality of task requests includes a set of sub-task requests andwherein the computing device sends corresponding sets of sub-taskrequests to the set of execution units when task requests of theplurality of task requests are generated; storing, by the set ofexecution units, the corresponding sets of sub-task requests, whereineach of a first, a second, and a third execution unit of the set ofexecution unit stores, respectively, a first, a second, and a thirdsub-task request of each of the corresponding sets of sub-task requeststo produce, respectively, a first, a second, and a third plurality ofsub-task requests; respectively generating first, second, and thirdsub-task estimation data for the first, the second, and the thirdplurality of sub-task requests by the first, the second, and the thirdexecution units; determining, for each execution unit, an executionready status for a selected sub-task request of the set of sub-taskrequests when each the set of execution units has resources available toprocess the selected sub-task request; and respectively adjustingtiming, sequencing, or processing of the first, the second, and thethird plurality of sub-task requests by the first, the second, and thethird execution units based on the first, the second, and the thirdsub-task estimation data and a corresponding execution ready status, toproduce a first, a second, and a third plurality of partial results,wherein, due to one or more difference factors from a list of differencefactors, the first, the second, and the third execution units processthe first, the second, and the third plurality of sub-task requests atdifference paces, wherein the list of difference factors includesdifferences in amounts of data to be processed per sub-task request,processing capabilities, memory storage capabilities, and networkingcapabilities, and wherein at least one partial result of the first,second, and third plurality of partial results is based on dispersederror encoded data stored in the DSN.
 2. The method of claim 1 furthercomprises: sending the first, the second, and the third plurality ofpartial results to the computing device, wherein the computing devicecompiles the first, the second, and the third plurality of partialresults to produce a plurality of results.
 3. The method of claim 1further comprises: respectively storing the first, the second, and thethird plurality of sub-task requests in first, second, and third holdingqueues by the first, the second, and the third execution units; andrespectively transferring first, second, and third selected sub-taskrequests of the first, the second, and the third plurality of sub-taskrequests from the first, the second, and the third holding queues tofirst, second, and third execution queues prior to respectivelyexecuting the first, the second, and the third selected sub-taskrequests.
 4. The method of claim 1 further comprises: determining, bythe first execution unit, the execution ready status for a firstselected sub-task request of the first plurality of sub-task requestswhen the first execution unit has resources available to process thefirst selected sub-task request; determining, by the first executionunit, an execution not ready status for the first selected sub-taskrequest of the first plurality of sub-task requests when the firstexecution unit does not have the resources available to process thefirst selected sub-task request; determining, by the second executionunit, the execution ready status for a second selected sub-task requestof the second plurality of sub-task requests when the second executionunit has resources available to process the second selected sub-taskrequest; determining, by the second execution unit, an execution notready status for the second selected sub-task request of the secondplurality of sub-task requests when the second execution unit does nothave the resources available to process the second selected sub-taskrequest; determining, by the third execution unit, the execution readystatus for a third selected sub-task request of the third plurality ofsub-task requests when the third execution unit has resources availableto process the third selected sub-task request; and determining, by thethird execution unit, an execution not ready status for the thirdselected sub-task requests of the third plurality of sub-task requestswhen the third execution unit does not have the resources available toprocess the third selected sub-task request.
 5. The method of claim 4further comprises: when a quorum of the first, the second, and the thirdexecution units exists, wherein a quorum is reached when two of thefirst, the second, and the third selected sub-task requests have theexecution ready status: executing, by the quorum of the first, thesecond, and the third execution units, two of the first, the second, orthe third selected sub-task requests to produce two of a first, asecond, and a third partial results of the first, the second, and thethird plurality of partial results.
 6. The method of claim 5 furthercomprises: for the first, the second, or the third execution unit not inthe quorum of execution units, deleting the respective first, thesecond, and the third selected sub-task requests when the two of thefirst, the second, and the third partial results have been produced andthe remaining one of the first, the second, and the third selectedsub-task requests has the execution not ready status.
 7. Anon-transitory computer-readable storage medium comprises: at least onememory section that stores operational instructions that, when executedby one or more processing modules of one or more computing devices of adispersed storage network (DSN), causes the one or more computingdevices to: receive, over time and by a set of execution units of theDSN, a plurality of sets of sub-task requests from a computing device ofthe DSN, wherein the computing device generates a plurality of taskrequests, wherein a task request of the plurality of task requestsincludes a set of sub-task requests and wherein the computing devicesends corresponding sets of sub-task requests to the set of executionunits when task requests of the plurality of task requests aregenerated; store, by the set of execution units, the corresponding setsof sub-task requests, wherein each of a first, a second, and a thirdexecution unit of the set of execution unit stores, respectively, afirst, a second, and a third sub-task request of each of thecorresponding sets of sub-task requests to produce, respectively, afirst, a second, and a third plurality of sub-task requests;respectively generate first, second, and third sub-task estimation datafor the first, the second, and the third plurality of sub-task requestsby the first, the second, and the third execution units; determine, foreach execution unit, an execution ready status for a selected sub-taskrequest of the set of sub-task requests when each the set of executionunits has resources available to process the selected sub-task request;and respectively adjust timing, sequencing, or processing of the first,the second, and the third plurality of sub-task requests by the first,the second, and the third execution units based on the first, thesecond, and the third sub-task estimation data and a correspondingexecution ready status, to produce a first, a second, and a thirdplurality of partial results, wherein, due to one or more differencefactors from a list of difference factors, the first, the second, andthe third execution units process the first, the second, and the thirdplurality of sub-task requests at difference paces, wherein the list ofdifference factors includes differences in amounts of data to beprocessed per sub-task request, processing capabilities, memory storagecapabilities, and networking capabilities, and wherein at least onepartial result of the first, second, and third plurality of partialresults is based on dispersed error encoded data stored in the DSN. 8.The non-transitory computer-readable storage medium of claim 7 furthercomprises: the at least one memory section stores further operationalinstructions that, when executed by the one or more processing modules,causes the one or more computing devices of the DSN to: send the first,the second, and the third plurality of partial results to the computingdevice, wherein the computing device compiles the first, the second, andthe third plurality of partial results to produce a plurality ofresults.
 9. The non-transitory computer-readable storage medium of claim7 further comprises: the at least one memory section stores furtheroperational instructions that, when executed by the one or moreprocessing modules, causes the one or more computing devices of the DSNto: respectively store the first, the second, and the third plurality ofsub-task requests in first, second, and third holding queues by thefirst, the second, and the third execution units; and respectivelytransfer first, second, and third selected sub-task requests of thefirst, the second, and the third plurality of sub-task requests from thefirst, the second, and the third holding queues to first, second, andthird execution queues prior to respectively executing the first, thesecond, and the third selected sub-task requests.
 10. The non-transitorycomputer-readable storage medium of claim 7 further comprises: the atleast one memory section stores further operational instructions that,when executed by the one or more processing modules, causes the one ormore computing devices of the DSN to: determine, by the first executionunit, the execution ready status for a first selected sub-task requestof the first plurality of sub-task requests when the first executionunit has resources available to process the first selected sub-taskrequest; determine, by the first execution unit, an execution not readystatus for the first selected sub-task request of the first plurality ofsub-task requests when the first execution unit does not have theresources available to process the first selected sub-task request;determine, by the second execution unit, the execution ready status fora second selected sub-task request of the second plurality of sub-taskrequests when the second execution unit has resources available toprocess the second selected sub-task request; determine, by the secondexecution unit, an execution not ready status for the second selectedsub-task requests of the second plurality of sub-task requests when thesecond execution unit does not have the resources available to processthe second selected sub-task request; determine, by the third executionunit, the execution ready status for a third selected sub-task requestof the third plurality of sub-task requests when the third executionunit has resources available to process the third selected sub-taskrequest; and determine, by the third execution unit, an execution notready status for the third selected sub-task requests of the thirdplurality of sub-task requests when the third execution unit does nothave the resources available to process the third selected sub-taskrequest.
 11. The non-transitory computer-readable storage medium ofclaim 10 further comprises: the at least one memory section storesfurther operational instructions that, when executed by the one or moreprocessing modules, causes the one or more computing devices of the DSNto: when a quorum of the first, the second, and the third executionunits exists, wherein a quorum is reached when two of the first, thesecond, and the third selected sub-task requests have the executionready status: execute, by the quorum of the first, the second, and thethird execution units, two of the first, the second, or the thirdselected sub-task requests to produce two of a first, a second, and athird partial results of the first, the second, and the third pluralityof partial results.
 12. The non-transitory computer-readable storagemedium of claim 11 further comprises: the at least one memory sectionstores further operational instructions that, when executed by the oneor more processing modules, causes the one or more computing devices ofthe DSN to: for the first, the second, or the third execution unit notin the quorum of execution units, delete the respective first, thesecond, and the third selected sub-task requests when the two of thefirst, the second, and the third partial results have been produced andthe remaining one of the first, the second, and the third selectedsub-task requests has the execution not ready status.
 13. An executionunit of a dispersed storage network (DSN), the execution unit comprises:an interface; a memory; and a processing module operably coupled to theinterface and the memory, wherein the processing module functions to:receive, over time via the interface, a plurality of sub-task requestsfrom a computing device of the DSN, wherein a plurality of sets ofsub-task requests includes the plurality of sub-task requests, whereinthe computing device generates a plurality of task requests, wherein atask request of the plurality of task requests includes a set ofsub-task requests, wherein the computing device sends corresponding setsof sub-task requests to a set of execution units of the DSN when taskrequests of the plurality of task requests are generated and wherein theset of execution units includes the execution unit; store, in thememory, a corresponding sub-task request of each of the correspondingsets of sub-task requests to produce the plurality of sub-task requests;generate sub-task estimation data for the plurality of sub-taskrequests; determine, for each execution unit, an execution ready statusfor a selected sub-task request of the set of sub-task requests wheneach the set of execution units has resources available to process theselected sub-task request; and adjust timing, sequencing, or processingof the plurality of sub-task requests based on the sub-task estimationdata and a corresponding execution ready status, to produce a pluralityof partial results, wherein, due to one or more difference factors froma list of difference factors, the execution unit process the pluralityof sub-task requests at difference paces, wherein the list of differencefactors includes differences in amounts of data to be processed persub-task request, processing capabilities, memory storage capabilities,and networking capabilities, and wherein at least one partial result ofa first, second, and third plurality of the partial results is based ondispersed error encoded data stored in the DSN.
 14. The execution unitof claim 13, wherein the processing module further functions to: send,via the interface, the plurality of partial results to the computingdevice, wherein the computing device compiles the plurality of partialresults to produce a plurality of results.
 15. The execution unit ofclaim 13, wherein the processing module further functions to: store theplurality of sub-task requests in a holding queue of the memory; andtransfer selected sub-task requests of the plurality of sub-taskrequests from the holding queue to an execution queue prior to executingthe selected sub-task requests.
 16. The execution unit of claim 13,wherein the processing module further functions to: determine theexecution ready status for a selected sub-task request of the pluralityof sub-task requests when the execution unit has resources available toprocess the selected sub-task request; and determine an execution notready status for the selected sub-task request of the plurality ofsub-task requests when the execution unit does not have the resourcesavailable to process the selected sub-task request.
 17. The executionunit of claim 16, wherein the processing module further functions to:when a quorum of the execution unit and another execution unit of theset of execution units exists, wherein a quorum is reached when theexecution unit has the execution ready status for the selected sub-taskrequest and the other execution has the execution ready status for acorresponding second selected sub-task request of the set of sub-taskrequests and wherein the set of sub-task requests includes the selectedsub-task request and the second selected sub-task request: execute theselected sub-task requests to produce a partial result of the pluralityof partial results.
 18. The execution unit of claim 17, wherein theprocessing module further functions to: when another quorum is reachedwithout inclusion of the execution unit, delete the selected sub-taskrequest when other partial results have been produced and the selectedsub-task requests has the execution not ready status.